---
language:
- lus
license: apache-2.0
base_model: Qwen/Qwen2-Audio-7B
tags:
- generated_from_trainer
datasets:
- andrewbawitlung/mizonal-v3
metrics:
- wer
- cer
model-index:
- name: qwen3-asr-0.6b-mizonal3-E2-lus-v2026.06
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: MiZonal v3
      type: andrewbawitlung/mizonal-v3
      config: default
      split: test
    metrics:
    - name: Wer
      type: wer
      value: 19.4954
    - name: Cer
      type: cer
      value: 4.4174
    - name: Real Time Factor
      type: rtf
      value: 0.0660
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# qwen3-asr-0.6b-mizonal3-E2-lus-v2026.06

This model is a fine-tuned version of [Qwen/Qwen2-Audio-7B](https://huggingface.co/Qwen/Qwen2-Audio-7B) on the **MiZonal v3** dataset. 
Note: ~1 hour of conversational speech was added to this dataset version.

It achieves the following results on the evaluation set:
- Wer: 19.4954
- Cer: 4.4174
- Real Time Factor: 0.0660

## Quick Inference


```python
import torch
import librosa
from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration

device = "cuda" if torch.cuda.is_available() else "cpu"

processor = AutoProcessor.from_pretrained("andrewbawitlung/qwen3-asr-0.6b-mizonal3-E2-lus-v2026.06")
model = Qwen2AudioForConditionalGeneration.from_pretrained("andrewbawitlung/qwen3-asr-0.6b-mizonal3-E2-lus-v2026.06").to(device)

audio, sr = librosa.load("your_audio.wav", sr=16000)

conversation = [
    {"role": "user", "content": [
        {"type": "audio", "audio_url": "your_audio.wav"},
        {"type": "text", "text": "Transcribe the audio:"}
    ]}
]
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
inputs = processor(text=text, audios=[audio], return_tensors="pt", padding=True)
inputs.input_ids = inputs.input_ids.to(device)

with torch.no_grad():
    generate_ids = model.generate(**inputs, max_length=256)

generate_ids = generate_ids[:, inputs.input_ids.size(1):]
transcription = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(transcription)
```


## Model description

### Experiment Configurations
This repository is part of a series of experiments. The different configurations are:
- **E1 (Baseline):** Standard training configuration.
- **E2 (Noise):** Training with background noise augmentation.
- **E3 (Speed):** Training with speed perturbation augmentation.
- **E4 (SpecAug):** Training with SpecAugment (time and frequency masking).
- **E5 (Combined):** Training with a combination of all augmentations.

### All Models in this Family
| Experiment | Hugging Face Repository |
| :--- | :--- |
| **E1 (Baseline)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E1-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E1-lus-v2026.06) |
| **E2 (Noise)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E2-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E2-lus-v2026.06) |
| **E3 (Speed)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E3-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E3-lus-v2026.06) |
| **E4 (SpecAug)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E4-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E4-lus-v2026.06) |
| **E5 (Combined)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E5-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E5-lus-v2026.06) |

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: OptimizerNames.ADAMW_TORCH_FUSED
- lr_scheduler_type: SchedulerType.LINEAR
- num_epochs: 8

### Training results

| step | epoch | train_loss | eval_loss | eval_wer | eval_cer | learning_rate | grad_norm |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 200 | 0.18 | 0.45 | 0.75 | 0.53 | 0.16 | 1.99e-05 | 14.50 |
| 400 | 0.36 | 0.27 | 0.40 | 0.32 | 0.08 | 1.95e-05 | 10.81 |
| 600 | 0.55 | 0.17 | 0.34 | 0.28 | 0.06 | 1.90e-05 | 7.56 |
| 800 | 0.73 | 0.21 | 0.30 | 0.25 | 0.06 | 1.86e-05 | 5.78 |
| 1000 | 0.91 | 0.14 | 0.29 | 0.24 | 0.05 | 1.81e-05 | 7.38 |
| 1200 | 1.09 | 0.09 | 0.27 | 0.23 | 0.05 | 1.76e-05 | 5.66 |
| 1400 | 1.28 | 0.07 | 0.26 | 0.22 | 0.05 | 1.72e-05 | 3.84 |
| 1600 | 1.46 | 0.08 | 0.26 | 0.21 | 0.05 | 1.67e-05 | 4.44 |
| 1800 | 1.64 | 0.08 | 0.25 | 0.20 | 0.05 | 1.62e-05 | 8.81 |
| 2000 | 1.82 | 0.06 | 0.25 | 0.20 | 0.05 | 1.58e-05 | 4.59 |
| 2200 | 2.00 | 0.05 | 0.24 | 0.20 | 0.05 | 1.53e-05 | 3.84 |
| 2400 | 2.19 | 0.04 | 0.26 | 0.20 | 0.05 | 1.48e-05 | 5.66 |
| 2600 | 2.37 | 0.03 | 0.26 | 0.19 | 0.05 | 1.44e-05 | 3.44 |
| 2800 | 2.55 | 0.03 | 0.27 | 0.19 | 0.04 | 1.39e-05 | 7.22 |
| 3000 | 2.73 | 0.03 | 0.26 | 0.20 | 0.05 | 1.34e-05 | 4.22 |
| 3200 | 2.91 | 0.05 | 0.26 | 0.19 | 0.04 | 1.30e-05 | 4.34 |
| 3400 | 3.10 | 0.02 | 0.28 | 0.19 | 0.04 | 1.25e-05 | 4.47 |
| 3600 | 3.28 | 0.01 | 0.29 | 0.19 | 0.04 | 1.20e-05 | 2.52 |
| 3800 | 3.46 | 0.02 | 0.29 | 0.19 | 0.04 | 1.16e-05 | 2.94 |
| 4000 | 3.64 | 0.01 | 0.29 | 0.19 | 0.04 | 1.11e-05 | 2.06 |
| 4200 | 3.83 | 0.01 | 0.30 | 0.19 | 0.04 | 1.07e-05 | 4.19 |
| 4400 | 4.01 | 0.01 | 0.30 | 0.19 | 0.04 | 1.02e-05 | 5.16 |
| 4600 | 4.19 | 0.01 | 0.31 | 0.19 | 0.04 | 9.72e-06 | 1.23 |
| 4800 | 4.37 | 0.00 | 0.31 | 0.19 | 0.04 | 9.26e-06 | 1.21 |
| 5000 | 4.55 | 0.01 | 0.32 | 0.19 | 0.04 | 8.79e-06 | 0.94 |
| 5200 | 4.74 | 0.00 | 0.32 | 0.19 | 0.04 | 8.33e-06 | 1.05 |
| 5400 | 4.92 | 0.00 | 0.32 | 0.19 | 0.04 | 7.86e-06 | 0.99 |
| 5600 | 5.10 | 0.00 | 0.33 | 0.19 | 0.04 | 7.40e-06 | 0.46 |
| 5800 | 5.28 | 0.00 | 0.33 | 0.19 | 0.04 | 6.94e-06 | 0.70 |
| 6000 | 5.46 | 0.00 | 0.33 | 0.19 | 0.04 | 6.47e-06 | 0.84 |
| 6200 | 5.65 | 0.00 | 0.34 | 0.19 | 0.04 | 6.01e-06 | 0.60 |
| 6400 | 5.83 | 0.00 | 0.34 | 0.19 | 0.04 | 5.54e-06 | 2.16 |
| 6600 | 6.01 | 0.00 | 0.34 | 0.19 | 0.04 | 5.08e-06 | 0.46 |
| 6800 | 6.19 | 0.00 | 0.34 | 0.19 | 0.04 | 4.61e-06 | 0.35 |
| 7000 | 6.38 | 0.00 | 0.34 | 0.19 | 0.04 | 4.15e-06 | 0.38 |
| 7200 | 6.56 | 0.00 | 0.34 | 0.19 | 0.04 | 3.68e-06 | 0.61 |
| 7400 | 6.74 | 0.00 | 0.34 | 0.19 | 0.04 | 3.22e-06 | 0.41 |
| 7600 | 6.92 | 0.00 | 0.34 | 0.19 | 0.04 | 2.75e-06 | 0.35 |
| 7800 | 7.10 | 0.00 | 0.34 | 0.19 | 0.04 | 2.29e-06 | 0.43 |
| 8000 | 7.29 | 0.00 | 0.34 | 0.19 | 0.04 | 1.82e-06 | 0.52 |
| 8200 | 7.47 | 0.00 | 0.34 | 0.19 | 0.04 | 1.36e-06 | 0.28 |
| 8400 | 7.65 | 0.00 | 0.34 | 0.19 | 0.04 | 8.95e-07 | 0.33 |
| 8600 | 7.83 | 0.00 | 0.34 | 0.19 | 0.04 | 4.30e-07 | 0.74 |